The present invention relates to electronic circuits, and, more particularly, to an electronic circuit for controlling movement through a fuzzy cellular architecture. That is, the invention relates to an electronic circuit for generating and controlling the movements of a multi-actuator electro-mechanical system. For example, such system may be of a type associated with a cellular architecture including a plurality of programmable cells interconnected locally to provide a logic/mathematical representation of the locomotive phenomenon. A corresponding conversion of the representation to commands or electric pulses for driving a plurality of actuators associated with the cells may be provided thereby.
The invention also concerns a method for generating and controlling the movements of a multi-actuator electro-mechanical system. Such system provides a logic/mathematical representation of the locomotive phenomenon and a corresponding conversion of the representation to commands or electric pulses through a cellular architecture associated with a plurality of actuators.
While the invention relates to the aspects of movement control in multi-actuator electro-mechanical systems, such as those used in robotics and industrial automation, it may also be applied to the movement of other mechanical systems. The following description makes reference to the above field of application for convenience of explanation only.
As is well known in the art, recent developments in the neuro-biology field have shown through experimentation the principles which underlie the movement of certain invertebrates. Moreover, the details of movement at cellular and molecular levels are also being defined through mathematical equations. Such developments have been reported, for example, in an article entitled xe2x80x9cMathematical Biologyxe2x80x9d by J. D. Murray, published by Springer-Verlag, Berlin, 1989, and in xe2x80x9cCommon Principles of Motor Control in Vertebrates and Invertebratesxe2x80x9d by G. K. Pearson in Ann. Rev. Neurosci., Vol. 16, pages 265-297, 1993.
The phenomena behind the movement of invertebrates, i.e., signal propagation to the nervous tissues, can now be described by particular solutions of differential equations to the partial derivatives in non-linear form. These equations are known as reaction diffusion equations. The nervous impulse generating/propagating mechanism can be related to a mathematical model including simultaneous differential equations of the second order generating local oscillations adapted to be diffused spatially through a suitable set of interactions between adjacent cells.
A reaction diffusion equation is expressed analytically by the following formulation:                                           δ            ⁢                          xe2x80x83                        ⁢            u                                δ            ⁢                          xe2x80x83                        ⁢            t                          =                                            f              ⁢                              xe2x80x83                            ⁢                              (                u                )                                      +                          D              ⁢                                                ∇                  2                                ⁢                u                            ⁢                              xe2x80x83                            ⁢              and              ⁢                              xe2x80x83                            ⁢                                                ∇                  2                                ⁢                u                                              =                                                    δ                ⁢                                  xe2x80x83                                ⁢                u                                            S                ⁢                                  xe2x80x83                                ⁢                                  x                  2                                                      +                                          δ                ⁢                                  xe2x80x83                                ⁢                u                                            δ                ⁢                                  xe2x80x83                                ⁢                                  y                  2                                                                                        (        1        )            
where f(u) is a non-linear function of the reaction state xe2x80x9cu,xe2x80x9d called a reaction function, and the term ∇2u is a Laplace operator, called xe2x80x9cLaplacian,xe2x80x9d and represents a weighed diffusion term by a diffusion coefficient D.
The particular solutions of these equations, namely u xcex5 m, f xcex5 m, D xcex5 mxm, describe wave phenomena based on self-sustained oscillations which have all the features of a nervous impulse, i.e., an autonomous wave or auto-wave. Peculiar to auto-waves are features such as propagation through non-linear active media at the expense of the energy thereof, preservation of the wave amplitude and shape, absence of reflections from the walls, annihilation upon collision, and slow-fast dynamics.
In less differentiated living organisms, such as polychaeta and mollusks, it has been observed that motion is generated by activating or xe2x80x9cfiringxe2x80x9d an extremely small number of neural cells forming part of the outer membrane of the animal. Furthermore, this outer membrane can synchronize itself together with the impulses generated by the xe2x80x9cmotor neurons.xe2x80x9d
Based upon these observations, it may be said that the most archaic version of movement involves propagating a nervous impulse along a membrane which is resistant to a medium, such as water or ground, where the locomotion takes place. In the simplest of moving organisms (e.g., certain mollusks and cuttlefishes) the locomotion is induced directly by propagation of the nervous impulse or auto-wave between neural cells which are distributed along the parts of the body that produce the movement.
In more developed animals, such as insects, the neural structure responsible for locomotion is organized at a higher level, but is still based upon reaction diffusion (RD) phenomena. In such organisms, the central nervous system includes a so-called central pattern generator (CPG) that is responsible for the transitions among different types of motion (slow, fast, swimming, etc.).
From an analytical standpoint, motion is determined, at a local level, by the propagation of an auto-wave through the locomotive neurons. At a central level, the type of the auto-wave is determined by a dynamic phenomenon of trajectories or spatial patterns known as a Turing pattern. This has been described in an article entitled xe2x80x9cThe Chemical Basis of Morphogenesisxe2x80x9d by A. Turing.
The above biological phenomena have been investigated mainly for analytical purposes. More recently, however, the principles that underlie the movement of some invertebrates have been studied with a view toward applying them to the movement of mechanical or electro-mechanical systems. The movement of all mechanical systems used in robotics and industrial automation is currently provided by digital microprocessor systems arranged to control the synchronization of movement. These digital systems are programmed to allow the mechanical system driven thereby to perform a predetermined sequence of operations and follow preset paths of movement.
While being advantageous in many ways, current approaches have several drawbacks. For example, the cost of the control arrangement increases with the complexity of the movement involved, i.e., with the number of actuators and state variables to be controlled. Also, the actuating system structure has a low flexibility.
An analytical representation and a mathematical model describing the phenomenon of locomotion in invertebrates has been used for implementing an analog device which allows the movement of multi-actuator mechanical systems to be controlled. This device is disclosed in European Patent Application No. 98830658.5 assigned to the assignee of the present invention. This device relates to a cellular architecture of the (RD-CNN) reaction diffusionxe2x80x94cellular neural network (RD-CNN). It is shown, in fact, that RD-CNN cells provide a good support with the reproduction of complex phenomena. This is, therefore, an analytical type of approach orientated towards an implementation by analog circuits where the value of the state variable of each RD-CNN cell represents the state of the individual entity that forms the structure, subjected to space discretization.
In particular, the circuitry described in the above reference is directed essentially to providing an analytical expression of the function f( ). That is, of the operator ∇2 through a transconductance function which can be programmed using control voltage values for each parameter. While providing numerous advantages, it may be difficult to implement in a digital device built with VLSI CMOS technology. In addition, pressing demands for control of the movement of multi-actuator systems require a modular construction of the control apparatus which can be re-configured to suit the layout of the control actuator network. However, this cannot be achieved with the approaches provided by the prior art.
The underlying technical problem of this invention is to provide an electronic circuit with structural and functional features that allow the movements of a multi-actuator electro-mechanical system to be controlled, and the limitations and drawbacks of prior art digital control systems to be overcome.
The concept behind the present invention involves using a cellular architecture having both algebraic and fuzzy calculation capabilities to provide space/time control of movement in a flexible, modular manner. In other words, the invention uses fuzzy microcontrollers with Boolean calculation capability to control the movement of multi-actuator mechanical systems.
According to the invention, a programmable electronic circuit for controlling movements of a multi-actuator electro-mechanical system of a type associated with a cellular architecture includes a plurality of actuators, a plurality of programmable cells associated with the plurality of actuators and interconnected locally, and a processing control unit including a fuzzy electronic microcontroller associated with the plurality of programmable cells. The plurality of programmable cells may provide a mathematical representation of a locomotive phenomenon and a corresponding conversion of the representation of the locomotive phenomenon to commands for driving the plurality of actuators.
More particularly, the processing control unit may perform Boolean calculations. Furthermore, the electronic circuit may include a first, volatile memory bi-directionally connected to the processing control unit, a second, non-volatile memory bi-directionally connected to the processing control unit, a third, non-volatile memory bi-directionally connected to the processing control unit, and an analog-to-digital converter bi-directionally connected to the processing control unit. The analog-to-digital converter may provide demultiplexing functions for driving the plurality of actuators.
Additionally, the processing control unit may work on a fuzzy model of a cell having at least two state variables. Four input variables coincident at a time are processed with a value provided by the at least two state variables of the cell at the time, and two variables are output. Also, each actuator may be driven by a control signal based upon a state of a single cell, where the control signal is updated by the processing control unit.
A method of controlling movements of a multi-actuator electro-mechanical system according to the invention includes providing a mathematical representation of a locomotive phenomenon, converting the representation of the locomotive phenomenon to commands through a cellular architecture associated with a plurality of actuators, and providing resolution of fuzzy inference rules using the cellular architecture. The resolution may be provided by updating the commands at a processing time responsive to a type of movement to be provided by the plurality of actuators.